1 visual analytics techniques that enable knowledge discovery: detect the expected and discover the...
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Visual Analytics Techniques that Enable Knowledge Discovery:
Detect the Expected and Discover the Unexpected
Jim J. ThomasDirector, National Visualization and Analytics Center
AAAS Fellow, Pacific Northwest National Laboratory Fellowhttp://NVAC.pnl.gov
ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery
VAKD '09 Paris, France
Visual Analytics Techniques that Enable Knowledge Discovery
Introduction: what is and is not visual analytics?Landscape of visualization scienceDiscussion of selected existing systems and technologiesCommon characteristics enabling knowledge discoveryTop ten challenges
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Introduction:History of Graphics and Visualization• 70s to 80s
– CAD/CAM Manufacturing, cars, planes, and chips– 3D, education, animation, medicine, etc.
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• 80s to 90s– Scientific visualization– Realism, entertainment
• 90s to 2000s– Information visualization– Web and Virtual environments
• 2000s to 2010s– Visual Analytics– Visual/audio analytic appliances
Visual Analytic Collaborations
Detecting the Expected -- Discovering the UnexpectedTM
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Visual Analytics Definition
Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces.
People use visual analytics tools and techniques to Synthesize information and derive insight from massive,
dynamic, ambiguous, and often conflicting data Detect the expected and discover the unexpected Provide timely, defensible, and understandable assessments Communicate assessment effectively for action.
“The beginning of knowledge is the discovery of something we do not understand.” ~Frank Herbert (1920 - 1986) 5
What is not visual analytics?
Large graph structure with no labels
Heat map with no labels
Search and retrieval systems
Chart with no interaction
Image with no semantic interpretation
Stand alone image that does not tell a story
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The Landscape of Visualization Science
Publications from IEEE VisWeek, 2006, 2007, 2008using IN-SPIRE Visual Analytics Tool
Each dot is an published science article, full text
Systems Considered:
IN-SPIRE - http://in-spire.pnl.gov.
JIGSAW - John Stasko, Carsten Görg, and Zhicheng Liu, “Jigsaw: Supporting Investigative Analysis through Interactive Visualization,” Information Visualization, vol. 7, no. 2, pp. 118-132, Palgrave Magellan,
2008.
WIREVIZ - Remco Chang, Mohammad Ghoniem, Robert Korsara, William Ribarsky, Jing Yang, Evan Suma, Carolina Ziemkiewicz, Daniel Keim, Agus Sudjianto, IEEE Visual Analytics Science and Technology
(VAST) 2007.
GreenGrid - Pak Chung Wong, Kevin Schneider, Patrick Mackey, Harlan Foote, George Chin Jr., Ross Guttromson, Jim Thomas “A Novel Visualization Technique for Electric Power Grid Analytics,” IEEE Transactions on Visualization and Computer Graphics 15(3):410-423.
Scalable Reasoning System - Pike WA, JR Bruce, RL Baddeley, DM Best, L Franklin, RA May, II, DM Rice, RM Riensche, and K Younkin. (2008) "The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics." In IEEE Symposium on Visual Analytics Science and Technology (VAST).
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Whole - Part Relationship
Scale independent representations, whole and parts at same time at multiple levels of abstraction, often linked
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Whole - Part Relationship
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Relationship Discovery
Explore high dimensional relationships, theme groupings, outlier detection, searching by proximity at multiple scales
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Relationship Discovery
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Boolean
By Example
Combined Exploratory and Confirmatory Analytics
Develop and refine hypothesis
Evidence collection, management, and matching to hypothesis
Tailor views/displays for thematic/hypothesis focus of interest
Often suggestive of predictions enabling proactive thinking
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Multiple Data Types
Supports multiple data types: structured/unstructured textImagery/video, cyberSystems of either data type or application specific
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Temporal Views and Interactions
Most analytics situations involve time, pace, velocityGroup segments of thoughts by timeCompare time segmentsOften combined with geospatial
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Reasoning Workspace
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Workspace to construct logic and illustrate reasoningFlexible spatial view of reasoning: stories
Stu Card, PARC
Grouping and Outlier DetectionForm groups of thought/dataLabels and annotationCompare groupingsFind small groups or outliers
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LabelingCritically important, Dynamic in scope, number labels, size, colorPositioningAlmost everything has labelsLabels tell semantic meaning
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Multiple Linked Views
Temporal, geospatial, theme, cluster, list views with association linkages between views
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Multiple Linked ViewsTemporal, geospatial, theme, cluster, list views with association linkages between views
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Heatmap View(Accounts to Keywords Relationship)
Strings and Beads(Relationships over Time)
Search by Example (Find Similar Accounts)
Keyword Network(Keyword Relationships)
WireViz Video
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Reporting
Capture display segments in graph modes for putting in reports, PPT etcCapture reasoning segments of analytic resultsCapture animations
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Engaging Interaction
GreenGrid video
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Alberta
North California
Southern
Northern
GreenGrid Video
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Tested With Known Data and Solutions
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Top Ten Challenges Within Visual Analytics
Human Information Discourse for Discovery—new interaction paradigm based around cognitive aspects of critical thinking
New visual paradigms that deal with scale, multi-type, dynamic streaming temporal data flows
Data, Information and Knowledge Representation
Collaborative Predictive/Proactive Visual Analytics
Visual Analytic Method Capture and Reuse
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Top Ten Challenges Within Visual Analytics
Dissemination and Communication
Visual Temporal Analytics
Validation/verification with test datasets openly available
Delivering short-term products while keeping the long view
Interoperability interfaces and standards: multiple VAC suites of tools
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Conclusions
Visual Analytics is an opportunity worth considering
Practice of Interdisciplinary Science is required
Broadly applies to many aspects of society
For each of you:
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The best is yet to come…